Discrimination of cheyne stokes breathing patterns

Abstract

A method of a processor for detecting a presence of Cheyne-Stokes respiration from a respiration signal includes accessing data representative of a respiration signal. Data is assessed to detect apnea and/or hypopnea events. A cycle length histogram is determined based on the events and an incident of Cheyne-Stokes respiration is detected based on the cycle length histogram.

The invention claimed is: 
     
       1. A method implemented by a processor for detecting a presence of Cheyne-Stokes respiration from a respiratory signal generated by a respiration sensor, the method comprising:
 accessing respiratory data representative of the respiratory signal; 
 assessing the accessed respiratory data to detect apnea and/or hypopnea events; 
 evaluating respiratory data directly following the detected events to estimate a jump feature representing a change in the respiratory data, wherein the jump feature is calculated by selecting a first peak of the respiratory data, selecting a second peak at a predetermined ratio of the first peak, and calculating a gradient between the first peak and the second peak; 
 detecting an incident of Cheyne-Stokes respiration based on the jump feature; and 
 responding to the detection of an incident of Cheyne-Stokes respiration, the responding comprising controlling an adjustment of a therapeutic pressure delivered by a respiratory treatment apparatus. 
 
     
     
       2. The method of  claim 1 , wherein evaluating the respiratory data comprises calculating inspiratory tidal volumes during at least a portion of time between two adjacent apnea and/or hypopnea events. 
     
     
       3. The method of  claim 1 , wherein evaluating the respiratory data comprises calculating a product of peak inspiratory flow data and inspiratory tidal volume and storing them in a morphology vector. 
     
     
       4. The method of  claim 3 , wherein evaluating the respiratory data further comprises computing a mean squared error between the morphology vector and an approximating function. 
     
     
       5. The method of  claim 3 , wherein evaluating the respiratory data comprises integrating the peak inspiratory flow data between apnea and/or hypopnea events and storing them in the morphology vector. 
     
     
       6. The method of  claim 5 , wherein evaluating the respiratory data further comprises computing a mean squared error between the morphology vector and an approximating function. 
     
     
       7. The method of  claim 1 , wherein the jump feature represents at least one of a rise and a fall of a breathing drive of a patient. 
     
     
       8. The method of  claim 1 , further comprising scaling the gradient between the first peak and the second peak. 
     
     
       9. The method of  claim 1 , wherein detecting the incident of Cheyne Stokes respiration comprises determining a Cheyne Stokes respiration probability using the jump feature. 
     
     
       10. An apparatus for detecting a presence of Cheyne-Stokes respiration from a respiratory signal generated by a respiration sensor, the apparatus comprising:
 a memory for storing respiratory data associated with the respiratory signal; and 
 a processor, coupled with the memory, the processor being configured to assess the respiratory data to detect apnea and/or hypopnea events;
 evaluate peaks in the respiratory data directly following the detected events to estimate a jump feature representing a change in the respiratory data, wherein the jump feature is calculated by selecting a first peak of the respiratory data, selecting a second peak at a predetermined ratio of the first peak, and calculating a gradient between the first peak and the second peak; 
 detect an incident of Cheyne-Stokes respiration based on the jump feature; and 
 generate a response to the detection of an incident of Cheyne-Stokes respiration, the response comprising an adjustment to therapeutic pressure delivered by a respiratory treatment apparatus. 
 
 
     
     
       11. The apparatus of  claim 10 , wherein the data associated with the respiratory signal comprises flow data and tidal volume data and the processor is further configured to:
 calculate peak inspiratory flow data and inspiratory tidal volume; and 
 calculate a product of the peak inspiratory flow data and inspiratory tidal volume and storing them in a morphology vector. 
 
     
     
       12. The apparatus of  claim 11 , wherein the processor is configured to normalize the morphology vector by converting it into 0 to 1 probability space. 
     
     
       13. The apparatus of  claim 11 , wherein the processor is configured to evaluate the peaks in the flow data by computing a mean squared error between the morphology vector and an approximating function. 
     
     
       14. The apparatus of  claim 11 , wherein the processor is configured to evaluate the peaks in the flow data by integrating the flow data between apnea and/or hypopnea events and storing them in the morphology vector. 
     
     
       15. The apparatus of  claim 14 , wherein the processor is configured to evaluate the peaks in the flow data by computing a mean squared error between the morphology vector and an approximating function. 
     
     
       16. The apparatus of  claim 10 , wherein the jump feature represents at least one of a rise and a fall of a breathing drive of a patient. 
     
     
       17. The apparatus of  claim 10 , wherein the processor is configured to scale the gradient between the first peak and the second peak. 
     
     
       18. The method of  claim 1 , wherein the accessed respiratory data comprises values of respiratory flow. 
     
     
       19. The method of  claim 1 , wherein the accessed respiratory data comprises values of ventilation. 
     
     
       20. The method of  claim 1 , wherein the accessed respiratory data comprises values of tidal volume.